Track bank chargeback sensitivity using disproportional risk level sampling design

ABSTRACT

Examples described herein generally relate to a computer device including a memory, and at least one processor configured to process a transaction. The computer device receives customer transaction information. The computer device evaluates the customer transaction information to determine a risk score for the transaction. The computer device assigns the transaction, based on the risk score, to one of a plurality of stratums including at least a first stratum and a second stratum. The computer device selects a merchant identifier (MID) from at least a first MID associated with the first stratum and a second MID associated with the second stratum based on at least the assigned stratum and a target chargeback rate for at least one of the first MID or the second MID. The computer device transmits the transaction information and the selected MID to an issuing bank.

BACKGROUND

The present disclosure relates to transaction processing, and moreparticularly to increasing transaction approval rates based on merchantrisk analysis.

In a traditional card-not-present transaction, a merchant collectstransaction data from the customer, usually using a form on the websitecompleted by the customer. The merchant traditionally validates thetransaction by forwarding the transaction data to a processor, whotransmits the transaction data to a retail bank (e.g., a card issuer)via a card network. The retail bank may perform a risk analysis on thetransaction data to determine whether to allow the transaction. If theretail bank determines that the transaction is risky due to fraud or apotential for chargebacks, the retail bank may deny the transaction.

A chargeback occurs when a transaction is charged to a card and the cardholder repudiates the transaction, for example, by alleging thetransaction was fraudulent or otherwise deficient. In a chargeback, themerchant must refund the purchase price, and may also lose the value ofthe merchandise that was shipped. The retail bank may also incur coststo resolve the chargeback. Accordingly, both merchants and retail bankshave an interest in preventing transactions that will result inchargebacks. For example, the merchant, the retail bank, or third partyproviders may conduct screening to predict a risk of a transactionresulting in a chargeback. A card network may limit the transactioninformation to certain fixed, pre-defined fields. Accordingly, a retailbank may be provided a limited data set upon which to determine whetherthe transaction is likely to result in a chargeback. A retail bank mayact conservatively and err toward rejecting transactions, which may leadto a higher rate of false positives (where valid transactions areincorrectly rejected), resulting in lost sales opportunities for themerchant.

Thus, there is a need in the art for improvements in card transactionprocessing.

SUMMARY

The following presents a simplified summary of one or moreimplementations of the present disclosure in order to provide a basicunderstanding of such implementations. This summary is not an extensiveoverview of all contemplated implementations, and is intended to neitheridentify key or critical elements of all implementations nor delineatethe scope of any or all implementations. Its sole purpose is to presentsome concepts of one or more implementations of the present disclosurein a simplified form as a prelude to the more detailed description thatis presented later.

In an example, the disclosure provides a computer device for processingtransactions. The computer device may include memory and a processingsystem comprising at least one processor communicatively coupled withthe memory. The processor may be configured to receive customertransaction information. The processor may be configured to evaluate thecustomer transaction information to determine a risk score for thetransaction. The processor may be configured to assign the transaction,based on the risk score, to one of a plurality of stratums, theplurality of stratums including at least a first stratum and a secondstratum. The processor may be configured to select a merchant identifier(MID) from at least a first MID associated with the first stratum and asecond MID associated with the second stratum based at least on theassigned stratum and a target chargeback rate for at least one of thefirst MID or the second MID. The processor may be configured to transmitthe transaction information and the selected MID to an issuing bank.

In another example, the disclosure provides a method executed by acomputer processor, of processing transactions. The method may includereceiving customer transaction information. The method may includeevaluating the customer transaction information to determine a riskscore for the transaction. The method may include assigning thetransaction, based on the risk score, to one of a plurality of stratums,the plurality of stratums including at least a first stratum and asecond stratum. The method may include selecting a MID from at least afirst MID associated with the first stratum and a second MID associatedwith the second stratum based at least on the assigned stratum and atarget chargeback rate for at least one of the first MID or the secondMID. The method may include transmitting the transaction information andthe selected MID to an issuing bank.

In another example, the disclosure provides a computer-readable medium.The computer-readable medium may include code executable by one or moreprocessors for processing a transaction. The computer-readable mediummay include code for receiving customer transaction information. Thecomputer-readable medium may include code for evaluating the customertransaction information to determine a risk score for the transaction.The computer-readable medium may include code for assigning thetransaction, based on the risk score, to one of a plurality of stratums,the plurality of stratums including at least a first stratum and asecond stratum. The computer-readable medium may include code forselecting a MID from at least a first MID associated with the firststratum and a second MID associated with the second stratum based atleast on the assigned stratum and a target chargeback rate for at leastone of the first MID or the second MID. The computer-readable medium mayinclude code for transmitting the transaction information and theselected MID to an issuing bank.

Additional advantages and novel features relating to implementations ofthe present disclosure will be set forth in part in the description thatfollows, and in part will become more apparent to those skilled in theart upon examination of the following or upon learning by practicethereof.

DESCRIPTION OF THE FIGURES

In the drawings:

FIG. 1 is a diagram of an example computer system for processing creditcard transactions, in accordance with an implementation of the presentdisclosure;

FIG. 2 is conceptual diagram illustrating an example dynamic MIDallocation, in accordance with an implementation of the presentdisclosure;

FIG. 3 is a conceptual diagram of a numerical example of the dynamic MIDallocation of FIG. 2, in accordance with an implementation of thepresent disclosure;

FIG. 4 is a flowchart of an example method of for processing credit cardtransactions, in accordance with an implementation of the presentdisclosure; and

FIG. 5 is a schematic block diagram of an example computer device, inaccordance with an implementation of the present disclosure.

DETAILED DESCRIPTION

The present disclosure provides systems and methods for processingtransactions by a merchant. The disclosure provides techniques thatallow a merchant to utilize the merchant's customer data to improveapproval rates of transactions and reduce false positive detections onthe part of an issuing bank. The present disclosure uses the example ofcredit card transactions using a credit card network. Other types ofcard transactions such as debit card signature transactions may beprocessed via a credit card network. The disclosed techniques may beapplicable to any transaction including a merchant identification number(MID) that is used to authorize or approve the transaction.

A merchant may have transaction contextual data that is not available toa credit card issuer. For example, a customer may maintain an accountwith the merchant and the merchant may have a history of customerinteractions with the merchant. The merchant may also operate a webpageand obtain information regarding the customer's interaction with thewebsite prior to making a purchase. Additionally, the merchant may haveinformation regarding how a good or service is to be delivered to thecustomer. In a conventional credit card network, this transactioncontextual information is available to the merchant and is not availableto the credit card issuer due to limitations on the data transmitted viaa credit card network. Accordingly, the merchant may be able to evaluatethe risk of a particular transaction better than a credit card issuerbased on the data available to the merchant.

One technique to improve credit card approval rates is to utilizedifferent merchant identification numbers or merchant identifiers (MIDs)for credit card transactions. The MID is one data field that istransmitted in a conventional credit card transaction. Large merchantstypically use multiple MIDs to categorize a type of transaction,currency, or profit center of the merchant for accounting purposes. Amerchant that can identify low risk transactions may improve thetransaction allowance rate of a MID by assigning low risk transactionsto the MID. Such a MID may be referred to as a trusted MID. Theallowance rate of the trusted MID may improve over time as the creditcard issuer experiences a low number of chargebacks associated with thetrusted MID. A merchant may also inform the credit card issuer of apolicy associated with the trusted MID and request special treatment.

The use of a trusted MID, however, may not actually improve overalltransaction approval rate for a merchant. In order to assign low risktransactions to the trusted MID, the merchant may not assign the samelow risk transactions to other MIDs. Accordingly, the other MIDs mayexperience higher chargeback rates as the low risk transactions areremoved from the pool of transactions for the other MIDs. Over time, theother MIDs may experience a decline in allowance rate inverselyproportional to the improvement in the allowance rate of the trustedMID. Therefore, the overall allowance rate of credit card transactionsfor the merchant may remain unchanged.

Generally described, the present disclosure provides a merchanttransaction system that improves overall transaction allowance rate forthe merchant by assigning transactions to different MIDs based on riskscores and target chargeback rates. Each MID may be associated with atarget chargeback rate. A first MID may be assigned a low targetchargeback rate selected to improve the approval rate of transactionsusing the first MID. A second MID may be assigned a target chargebackrate that is consistent with industry chargeback rates. Accordingly, thesecond MID may experience a constant standard approval rate. Individualtransactions may be assigned to a MID based on a risk score and aprobabilistic or proportional allocation to achieve the desiredchargeback rates.

Referring now to FIG. 1, an example merchant transaction system 100includes a computer device 110. The computer device 110 may be, forexample, any mobile or fixed computer device including but not limitedto a computer server, desktop or laptop or tablet computer, a cellulartelephone, a personal digital assistant (PDA), a handheld device, anyother computer device having wired and/or wireless connection capabilitywith one or more other devices, or any other type of computerized devicecapable of processing credit card transactions.

The computer device 110 may include a central processing unit (CPU) 114that executes instructions stored in memory 116. For example, the CPU114 may execute an operating system 140 and one or more applications130, which may include a transaction processing application 150. Thecomputer device 110 may also include a network interface 120 forcommunication with external devices via a network. For example, thecomputer device 110 may communicate with a bank system 170 of a creditcard issuer via a credit card network 172.

The computer device 110 may include a display 122. The display 122 maybe, for example, a computer monitor or a touch-screen. The display 122may provide information to an operator and allow the operator toconfigure the computer device 110.

Memory 116 may be configured for storing data and/or computer-executableinstructions defining and/or associated with an operating system 140and/or application 130, and CPU 114 may execute operating system 140and/or application 130. Memory 116 may represent one or more hardwarememory devices accessible to computer device 110. An example of memory116 can include, but is not limited to, a type of memory usable by acomputer, such as random access memory (RAM), read only memory (ROM),tapes, magnetic discs, optical discs, volatile memory, non-volatilememory, and any combination thereof. Memory 116 may store local versionsof applications being executed by CPU 114. In an implementation, thememory 116 may include a storage device, which may be a non-volatilememory.

The CPU 114 may include one or more processors for executinginstructions. An example of CPU 114 can include, but is not limited to,any processor specially programmed as described herein, including acontroller, microcontroller, application specific integrated circuit(ASIC), field programmable gate array (FPGA), system on chip (SoC), orother programmable logic or state machine. The CPU 114 may include otherprocessing components such as an arithmetic logic unit (ALU), registers,and a control unit. The CPU 114 may include multiple cores and may beable to process different sets of instructions and/or data concurrentlyusing the multiple cores to execute multiple threads.

The operating system 140 may include instructions (such as applications130) stored in memory 116 and executable by the CPU 114. The operatingsystem 140 may include the transaction processing application 150 forassigning credit card transactions to MIDs. The transaction processingapplication 150 may handle transactions from one or more business unitsof a merchant.

The transaction processing application 150 may include a sales website152 for receiving customer credit card transaction information, a riskscoring component 154 for determining a risk score based on the customercredit card transaction information, and an acceptance optimizationmodule 160 for assigning a MID to the transaction.

The sales website 152 may be a website hosted by the computer device 110that facilitates a sales transaction. For example, the sales website 152may provide information about products or services offered by themerchant. The sales website 152 may provide a checkout feature thatprovides a user interface for a credit card customer to enter personaland credit card information. The sales website 152 may includeinstrumentation 153. For example, the instrumentation 153 may beprovided as a software development kit (SDK) for providing tools to thatmay be added to the sales website 152. The merchant may host the websiteand instrumentation 153 on one or more enterprise servers or cloudservers (e.g., computer device 110). The instrumentation 153 may providethe sales website 152 with capability to monitor a customer'sinteraction with the website and generate transaction contextual datafor a session (e.g., user interaction with the merchant website) leadingto the transaction. In an implementation, the transaction contextualdata may include a device fingerprint identifying a hardware device (orvirtual device) used to make a transaction. The device fingerprint maybe a fuzzy identification of a user device that may be applicable acrossmultiple sessions and properties (e.g., websites). The fuzzyidentification may not require any specific piece of identification, butmay instead may be based on a set of available information. Theavailable information may be hashed according to a defined algorithm togenerate the device fingerprint such that when the set of availableinformation is used for another transaction, the device can beidentified. For example, the device fingerprint may enable detection anddefense against bot behavior by detecting multiple transactionsperformed by the same device. Further, for example, the devicefingerprint may be used in fraud and abuse prevention scenariosincluding sign-up, login, account updates, checkout, and transactions.For instance, the instrumentation 153 may place tags on multiple pagesthat track user behavior and engagement using the device fingerprint.The tracked user behavior defined in the transaction contextual data maybe used to differentiate a bot from a human, identify risky behavior, orfind fraudsters using multiple accounts from the same machine. Asanother example of transaction contextual information, theinstrumentation 153 may determine a payment context. The payment contextmay include information regarding a user's account or payment details.For example, the payment context may include an age of the customer'saccount, whether the payment information is stored by the website,and/or how many times the payment information, or different paymentinformation for the customer, has been used at the website. The paymentcontext may also include product details. The transaction contextualdata generated by the sales website 152 and instrumentation 153 may beused by a merchant in evaluating risk of a transaction, but may not beavailable to a credit card issuer because only limited information canbe provided via a credit card network 172.

A risk scoring component 154 may execute a risk scoring algorithm todetermine a risk score based on the customer credit card transactioninformation and any other information available to the merchant,including transaction contextual information from instrumentation 153.The risk scoring component 154 may perform automated scoring using, forexample, advanced machine learned scoring, long-term and short-termmodeling, unsupervised learning, fraud sweeps, and anomaly detection.Each transaction may be given a score within a range. For example, therisk score may range from 1 to 10,000, with a lower score indicating alower risk.

The acceptance optimization module 160 may assign a MID to eachtransaction in order to optimize a transaction acceptance rate for themerchant. The acceptance optimization module 160 may include a stratumcomponent 162 that determines a stratum for the transaction based on arisk score. For example, the stratum of the transaction may be based onwhether the risk score exceeds a threshold. In an implementation, ablocked stratum may include transactions with risk scores above athreshold. The merchant may block any transaction in the blocked stratumand assign no MID to such transactions. A low risk stratum may includetransactions that are below a risk threshold. An acceptable risk stratummay include transactions with risk scores between the blocked thresholdand the risk threshold. Each stratum may have a chargeback rate that maybe determined statistically based on historical transactions for thestratum.

The acceptance optimization module 160 may improve an acceptance ratefor one or more stratums and the overall acceptance rate for themerchant by assigning transactions to MIDs in a manner that achieves atarget chargeback rate 164 for one or more MIDs. The acceptanceoptimization module 160 may statistically achieve the target chargebackrate by assigning transactions to each MID using a ratio of transactionsfrom each stratum that will statistically yield the desired chargebackrate. That is, the historical chargeback rate for each stratum may beused to predict the ratio of transactions that will yield the targetchargeback rate 164. The acceptance optimization module 160 may thenassign transactions to each MID according to the ratio of transactions.

The bank system 170 may be operated by an issuing bank, which may alsobe referred to as a credit card issuer or retail bank, to approve ordecline credit card transactions. The bank system 170 may receive creditcard transaction information via a credit card network 172. For example,the credit card network 172 may carry transaction data including cardinformation, transaction amount, merchant category code (MCC),geographic location, merchant identification number (MID), point ofsale, and terminal for each transaction. The credit card network 172 mayinclude multiple intermediaries. For example, the credit card network172 may include an acquirer, a network or association, and third partyservice providers (e.g., security services).

The bank system 170 may include an authorization component 174 thatdetermines whether to approve or decline a credit card transaction. Theauthorization component 174 may operate on the transaction informationprovided via the credit card network 172 to evaluate a risk of thecredit card transaction. For example, risks may include fraud andchargebacks. Since the transaction data is limited, the MID may be animportant field in risk evaluation.

FIG. 2 illustrates a dynamic MID allocation 200 that may be used byacceptance optimization module to allocate transactions between a firstMID 202 and a second MID 204. A merchant may use multiple MIDs to sorttransactions by a type of transaction, currency, or profit center. Thefirst MID 202 and the second MID 204 may be for transactions that areotherwise similar (e.g., same type of transaction, currency, and profitcenter) but are used to separate transactions based on risk stratum toimprove acceptance rates. The dynamic MID allocation 200 may include ablocked stratum 210, a default stratum 220, and a trusted stratum 230.The acceptance optimization module 160 may assign a stratum based on therisk score generated by risk scoring component 154. For example, theblocked stratum 210 may be assigned to a transaction with a risk scoreabove a blocking threshold 216 (e.g., a score of 1000 on the 1-10000scale). In an implementation, the blocking threshold 216 may be based ona level of risk that the merchant is willing to accept. On the oppositeend of the spectrum, the trusted stratum 230 may be assigned to atransaction with a score below a risk threshold 226. The risk threshold226 may be dynamically determined based on a chargeback rate oracceptance rate. For example, the risk threshold 226 may be set to ascore that results in transactions assigned to the trusted stratum 230having a desired chargeback rate (CB_(T)). The default stratum 220 maybe assigned to transactions having a risk score between the riskthreshold 226 and the blocking threshold 216. Transaction assigned tothe default stratum 220 may have a higher chargeback rate (CBD) thantransactions assigned to the trusted stratum 230. As mentioned above,assigning MIDs to transactions based solely on risk score stratum mayresult in a MID associated with the trusted stratum 230 experiencing anincreased approval rate, but a MID associated with the default stratum220 experiencing a decreased approval rate.

The acceptance optimization module 160 may cross-populate a first MID202 and a second MID 204 with transactions from the default stratum 220and the trusted stratum 230 to improve overall acceptance rate. Forexample, the trusted stratum 230 may be divided into a first trusted MIDgroup 232 and a second trusted MID group 234, and the default stratum220 may be divided into a first default MID group 222 and a seconddefault MID group 224. The first MID 202 may be designed to have atarget chargeback rate that results in a higher acceptance rate, whilethe second MID 204 may be designed to have a chargeback rate thatremains constant. In one implementation, for example, a targetchargeback rate for the first MID 202 may be 0.5% and a targetchargeback rate for the second MID 204 may be 2.0%. The targetchargeback rates may depend on a merchant transaction pool and tolerablerisk level (e.g., blocking threshold 216), which affect the overallchargeback rate for the merchant. The acceptance optimization module 160may cross-populate the first MID 202 and the second MID 204 based onproportions or ratios of total transactions assigned to each MID group.For example, the trusted stratum 230 may include a percentage of totaltransactions (p_(T)). The first trusted MID group 232 may be assigned apercentage of total transactions (p_(FT)), and the remainingtransactions (p_(T)−p_(FT)) may be assigned to the second trusted MIDgroup 234. Similarly, the default stratum may include a percentage oftotal transactions (p_(D)). The first default MID group 222 may beassigned a percentage of total transactions (p_(FD)), and the remainingtransactions (p_(D)−p_(FD)) may be assigned to the second default MIDgroup 224. Transactions in the first trusted MID group 232 and the firstdefault MID group 222 may be assigned to the first MID 202. Likewise,transaction in the second trusted MID group 234 and the second defaultMID group 224 may be assigned to the second MID 204.

An expected chargeback rate for the first MID may be:

$\begin{matrix}{{CB}_{F} = \frac{{p_{FT} \times {CB}_{T}} + {p_{FD} \times {CB}_{D}}}{p_{FT} + p_{FD}}} & (1)\end{matrix}$

An expected chargeback rate for the second MID may be:

$\begin{matrix}{{CB}_{S} = \frac{{\left( {p_{T} - p_{FT}} \right) \times {CB}_{T}} + {\left( {p_{D} - p_{FD}} \right) \times {CB}_{D}}}{\left( {p_{T} - p_{FT}} \right) + \left( {p_{D} - p_{FD}} \right)}} & (2)\end{matrix}$

FIG. 3 shows a numerical example 300 of the dynamic MID allocation 200.As illustrated, p_(T)=90, p_(D)=10, p_(FT)=80, p_(FD)=5, CB_(T)=0.2%,and CB_(F)=5%. According to formula (1), CB_(F)=0.5%. According toformula (2), CB_(S)=1.8%. Therefore, although the default stratum 220has a chargeback rate of 5%, by cross-populating with the trustedstratum 230 having a chargeback rate of 0.2%, the chargeback rate of thesecond MID (CB_(S)) may be only 1.8%, which may not result in a loweracceptance rate. The chargeback rate of the first MID (CB_(F)) mayremain low at 0.5%, which may result in a higher acceptance rate.

The parameters of the dynamic MID allocation 200 may be dynamicallyadjusted to meet particular acceptance rate or chargeback rate targets.For example, if the acceptance rate of the second MID begins to decline,CB_(S) may be reduced by decreasing p_(FT), thereby increasingp_(T)−p_(FT). If the change to p_(FT) would increase CB_(F) above atarget chargeback rate, the CB_(T) and CB_(D) may be reduced by loweringthe blocking threshold 216 and/or risk threshold 226.

Turning to FIG. 4, an example method 400 assigns MIDs to transactions.For example, method 400 may be performed by the transaction processingapplication 150 on the computer device 110.

At block 410, the method 400 may include receiving customer transactioninformation. For instance, in an implementation, the sales website 152may receive the customer transaction information. The transactioninformation may be entered by the customer. The transaction informationmay also include transaction contextual data collected byinstrumentation 153.

At block 420, the method 400 may include evaluating the customertransaction information to determine a risk score for the transaction.For instance, in an implementation, the risk scoring component 154 mayevaluate the customer transaction information to determine a risk scorefor the transaction.

At block 430, the method 400 may include assigning the transaction,based on the risk score, to one of a plurality of stratums. Theplurality of stratums may include at least a first stratum and a secondstratum. For instance, in an implementation, the stratum component 162may assign the transaction, based on the risk score from risk scoringcomponent 154, to one of a plurality of stratums 210, 220, 230 includingat least a first stratum (e.g., trusted stratum 230) and a secondstratum (e.g., default stratum 220). At block 432, the block 430 mayinclude determining whether the risk score satisfies a threshold. Thethreshold may be based on an acceptance rate of transactions satisfyingthe threshold. For example, the stratum component 162 may determinewhether the risk score satisfies one or both of the blocking threshold216 and the risk threshold 226.

At block 440, the method 400 may include selecting a MID from at least afirst MID associated with the first stratum and a second MID associatedwith the second stratum based at least on the assigned stratum and atarget chargeback rate for at least one of the first MID or the secondMID. For example, the acceptance optimization module 160 may select theMID from at least a first MID 202 associated with the first stratum 230and a second MID 204 associated with the second stratum 220 based on thetarget chargeback rate 164 for at least one of the first MID 202 or thesecond MID 204. A MID may be associated with a stratum based on anordinal priority of chargeback rate. For example, the trusted stratum230 may be a first stratum that has the lowest chargeback rate due tolower risk scores, and the first MID 202 may be designed to have thelowest chargeback rate (e.g., by having a high proportion oftransactions from the first stratum). Accordingly, the first MID 202 maybe associated with the trusted stratum 230. As another example, thedefault stratum may be a second stratum in order of chargeback rate, andthe second MID 204 may be designed to have the second lowest chargebackrate. Accordingly, the second MID 204 may be associated with the defaultstratum 220. In contrast, the blocked stratum 210 may not be associatedwith any MID. Further, association between a MID and a stratum does notnecessarily require a majority of the transactions for the MID to befrom the associated stratum. In the illustrated example of FIG. 3, thesecond MID 204 actually has more transactions from the trusted stratum230 due to the relatively larger number of transactions in the trustedstratum.

At block 442, the block 440 may include determining a ratio oftransactions in the first stratum and transactions in the second stratumto assign to the first MID 202 to achieve the target chargeback rate. Inan implementation, for example, the acceptance optimization module 160may determine a ratio of transactions in the first stratum (e.g., firsttrusted MID group 232) and transactions in the second stratum (e.g.,first default MID group 222) to assign to the first MID to achieve thetarget chargeback rate. The ratio may be based on the chargeback rateassociated with each stratum (e.g., CB_(T) and CB_(D)).

At block 444, the block 440 may include probabilistically assigning aMID for the transaction based on ratio of transaction. In animplementation, the acceptance optimization module 160 mayprobabilistically assign the MID for the transaction based on the ratioof transactions. Probabilistically assigning the MID to a singletransaction may include any technique that results in multipletransactions being assigned to the MIDs in the ratio of transactions.For example, an individual transaction may be assigned a random orpseudo-random number and then assigned to the MID by comparing therandom or pseudo-random number to a threshold based on the ratio oftransactions. As another example, transactions may be assigned to MIDsbased on an order determined by the ratio of transactions (e.g., everyother transaction for a 1:1 ratio). For an individual transaction, theassignment may have a probability of being assigned to each MID equal tothe ratio of transactions for the respective MID.

At block 450, the method 400 may include transmitting the transactioninformation and the selected MID to an issuing bank. For instance, in animplementation, the transaction processing application 150 may transmitthe transaction information and the selected MID to the issuing bank atbank system 170 via the network interface 120 and the credit cardnetwork 172. The transaction information and the selected MID may passthrough intermediaries such as an acquirer. In an implementation, theselected MID may be a field of the transaction information submitted tothe bank system 170.

At block 460, the method 400 may optionally include receiving anindication of a decision to approve or decline the transaction. Forexample, the transaction processing application 150 may receive theindication of the decision to approve or decline the transaction. Thetransaction processing application 150 may inform the customer of thedecision via the sales website 152.

At block 470, the method 400 may optionally include adjusting the targetchargeback rate based on the decision. For instance, the acceptanceoptimization module 160 may adjust the target chargeback rate 164 basedon the decision. The decision may have an incremental effect on theacceptance rate. For example, an accepted transaction may indicate thatthe acceptance rate is increasing and a declined transaction mayindicate that the acceptance rate is decreasing. The acceptanceoptimization module 160 may adjust the target chargeback rate 164 forone or more MIDs to improve the acceptance rate of the MID or anotherMID.

Referring now to FIG. 5, illustrated is an example computer device 110in accordance with an implementation, including additional componentdetails as compared to FIG. 1. In one example, computer device 110 mayinclude processor 48 for carrying out processing functions associatedwith one or more of components and functions described herein. Processor48 can include a single or multiple set of processors or multi-coreprocessors. Moreover, processor 48 can be implemented as an integratedprocessing system and/or a distributed processing system. In animplementation, for example, processor 48 may include CPU 114.

In an example, computer device 110 may include memory 50 for storinginstructions executable by the processor 48 for carrying out thefunctions described herein. In an implementation, for example, memory 50may include memory 116. The memory 50 may include instructions forexecuting the transaction processing application 150.

Further, computer device 110 may include a communications component 52that provides for establishing and maintaining communications with oneor more parties utilizing hardware, software, and services as describedherein. Communications component 52 may carry communications betweencomponents on computer device 110, as well as between computer device110 and external devices, such as devices located across acommunications network and/or devices serially or locally connected tocomputer device 110. For example, communications component 52 mayinclude one or more buses, and may further include transmit chaincomponents and receive chain components associated with a transmitterand receiver, respectively, operable for interfacing with externaldevices.

Additionally, computer device 110 may include a data store 54, which canbe any suitable combination of hardware and/or software, that providesfor mass storage of information, databases, and programs employed inconnection with implementations described herein. For example, datastore 54 may be a data repository for operating system 140 and/orapplications 130. The data store may include memory 116 and/or storagedevice 118.

Computer device 110 may also include a user interface component 56operable to receive inputs from a user of computer device 110 andfurther operable to generate outputs for presentation to the user. Userinterface component 56 may include one or more input devices, includingbut not limited to a keyboard, a number pad, a mouse, a touch-sensitivedisplay, a digitizer, a navigation key, a function key, a microphone, avoice recognition component, any other mechanism capable of receiving aninput from a user, or any combination thereof. Further, user interfacecomponent 56 may include one or more output devices, including but notlimited to a display, a speaker, a haptic feedback mechanism, a printer,any other mechanism capable of presenting an output to a user, or anycombination thereof.

In an implementation, user interface component 56 may transmit and/orreceive messages corresponding to the operation of operating system 140and/or applications 130. In addition, processor 48 may execute operatingsystem 140 and/or applications 130, and memory 50 or data store 54 maystore them.

As used in this application, the terms “component,” “system” and thelike are intended to include a computer-related entity, such as but notlimited to hardware, firmware, a combination of hardware and software,software, or software in execution. For example, a component may be, butis not limited to being, a process running on a processor, a processor,an object, an executable, a thread of execution, a program, and/or acomputer. By way of illustration, both an application running on acomputer device and the computer device can be a component. One or morecomponents can reside within a process and/or thread of execution and acomponent may be localized on one computer and/or distributed betweentwo or more computers. In addition, these components can execute fromvarious computer readable media having various data structures storedthereon. The components may communicate by way of local and/or remoteprocesses such as in accordance with a signal having one or more datapackets, such as data from one component interacting with anothercomponent in a local system, distributed system, and/or across a networksuch as the Internet with other systems by way of the signal.

Moreover, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom the context, the phrase “X employs A or B” is intended to mean anyof the natural inclusive permutations. That is, the phrase “X employs Aor B” is satisfied by any of the following instances: X employs A; Xemploys B; or X employs both A and B. In addition, the articles “a” and“an” as used in this application and the appended claims shouldgenerally be construed to mean “one or more” unless specified otherwiseor clear from the context to be directed to a singular form.

Various implementations or features may have been presented in terms ofsystems that may include a number of devices, components, modules, andthe like. A person skilled in the art should understand and appreciatethat the various systems may include additional devices, components,modules, etc. and/or may not include all of the devices, components,modules etc. discussed in connection with the figures. A combination ofthese approaches may also be used.

The various illustrative logics, logical blocks, and actions of methodsdescribed in connection with the embodiments disclosed herein may beimplemented or performed with a specially-programmed one of a generalpurpose processor, a digital signal processor (DSP), an applicationspecific integrated circuit (ASIC), a field programmable gate array(FPGA) or other programmable logic device, discrete gate or transistorlogic, discrete hardware components, or any combination thereof designedto perform the functions described herein. A general-purpose processormay be a microprocessor, but, in the alternative, the processor may beany conventional processor, controller, microcontroller, or statemachine. A processor may also be implemented as a combination ofcomputer devices, e.g., a combination of a DSP and a microprocessor, aplurality of microprocessors, one or more microprocessors in conjunctionwith a DSP core, or any other such configuration. Additionally, at leastone processor may comprise one or more components operable to performone or more of the steps and/or actions described above.

Further, the steps and/or actions of a method or procedure described inconnection with the implementations disclosed herein may be embodieddirectly in hardware, in a software module executed by a processor, orin a combination of the two. A software module may reside in RAM memory,flash memory, ROM memory, EPROM memory, EEPROM memory, registers, a harddisk, a removable disk, a CD-ROM, or any other form of storage mediumknown in the art. An exemplary storage medium may be coupled to theprocessor, such that the processor can read information from, and writeinformation to, the storage medium. In the alternative, the storagemedium may be integral to the processor. Further, in someimplementations, the processor and the storage medium may reside in anASIC. Additionally, the ASIC may reside in a user terminal. In thealternative, the processor and the storage medium may reside as discretecomponents in a user terminal. Additionally, in some implementations,the steps and/or actions of a method or procedure may reside as one orany combination or set of codes and/or instructions on a machinereadable medium and/or computer readable medium, which may beincorporated into a computer program product.

In one or more implementations, the functions described may beimplemented in hardware, software, firmware, or any combination thereof.If implemented in software, the functions may be stored or transmittedas one or more instructions or code on a computer-readable medium.Computer-readable media includes both computer storage media andcommunication media including any medium that facilitates transfer of acomputer program from one place to another. A storage medium may be anyavailable media that can be accessed by a computer. By way of example,and not limitation, such computer-readable media can comprise RAM, ROM,EEPROM, CD-ROM or other optical disk storage, magnetic disk storage orother magnetic storage devices, or any other medium that can be used tocarry or store desired program code in the form of instructions or datastructures and that can be accessed by a computer. Disk and disc, asused herein, includes compact disc (CD), laser disc, optical disc,digital versatile disc (DVD), floppy disk and Blu-ray disc where disksusually reproduce data magnetically, while discs usually reproduce dataoptically with lasers. Combinations of the above should also be includedwithin the scope of computer-readable media.

While implementations of the present disclosure have been described inconnection with examples thereof, it will be understood by those skilledin the art that variations and modifications of the implementationsdescribed above may be made without departing from the scope hereof.Other implementations will be apparent to those skilled in the art froma consideration of the specification or from a practice in accordancewith examples disclosed herein.

What is claimed is:
 1. A computer device for processing transactions,comprising: memory; and a processing system comprising at least oneprocessor communicatively coupled with the memory and configured to:receive customer transaction information; evaluate the customertransaction information to determine a risk score for the transaction;assign the transaction, based at least on the risk score, to one of aplurality of stratums, the plurality of stratums including at least afirst stratum and a second stratum; select a merchant identifier (MID)from at least a first MID associated with the first stratum and a secondMID associated with the second stratum based at least on the assignedstratum and a target chargeback rate for at least one of the first MIDor the second MID; and transmit the transaction information and theselected MID to an issuing bank.
 2. The computer device of claim 1,wherein selecting the MID comprises determining a ratio of transactionsin the first stratum and transactions in the second stratum to assign tothe first MID to achieve the target chargeback rate.
 3. The computerdevice of claim 2, wherein the ratio of transactions is based on apercentage of transactions in the first stratum, a chargeback rate ofthe first stratum, a percentage of transactions in the second stratum,and a chargeback rate of the second stratum.
 4. The computer device ofclaim 2, wherein selecting the MID comprises probabilistically assigningthe MID to the transaction based on the ratio of transactionscorresponding to the assigned stratum.
 5. The computer device of claim1, wherein the processor is configured to receive an indication of adecision to approve or decline the transaction.
 6. The computer deviceof claim 5, wherein the processor is configured to adjust the targetchargeback rate based on the indication of the decision.
 7. The computerdevice of claim 1, wherein the processor is configured to determinewhether the risk score satisfies a threshold for the stratum, whereinthe threshold is determined based on an acceptance rate of transactionssatisfying the threshold.
 8. A method of processing transactions,comprising: receiving customer transaction information; evaluating thecustomer transaction information to determine a risk score for thetransaction; assigning the transaction, based on the risk score, to oneof a plurality of stratums, the plurality of stratums including at leasta first stratum and a second stratum; selecting a merchant identifier(MID) from at least a first MID associated with the first stratum and asecond MID associated with the second stratum based on a targetchargeback rate for at least one of the first MID or the second MID; andtransmitting the transaction information and the selected MID to anissuing bank.
 9. The method of claim 8, wherein selecting the MIDcomprises determining a ratio of transactions in the first stratum andtransactions in the second stratum to assign to the first MID to achievethe target chargeback rate.
 10. The method of claim 9, wherein the ratioof transactions is based on a percentage of transactions in the firststratum, a chargeback rate of the first stratum, a percentage oftransactions in the second stratum, and a chargeback rate of the secondstratum.
 11. The method of claim 9, wherein selecting the MID comprisesprobabilistically assigning the MID to the transaction based on theratio of transactions corresponding to the assigned stratum.
 12. Themethod of claim 8, further comprising receiving an indication of adecision to approve or decline the transaction.
 13. The method of claim12, further comprising adjusting the target chargeback rate based on theindication of the decision.
 14. The method of claim 12, whereinassigning the transaction to the stratum based on the risk scorecomprises determining whether the risk score satisfies a threshold,wherein the threshold is determined based on an acceptance rate oftransactions satisfying the threshold.
 15. A computer-readable medium,comprising code executable by one or more processors for processing atransaction, the code comprising code for: receiving customertransaction information; evaluating the customer transaction informationto determine a risk score for the transaction; assigning thetransaction, based on the risk score, to one of a plurality of stratums,the plurality of stratums including at least a first stratum and asecond stratum; selecting a merchant identifier (MID) from at least afirst MID associated with the first stratum and a second MID associatedwith the second stratum based on a target chargeback rate for at leastone of the first MID or the second MID; and transmitting the transactioninformation and the selected MID to an issuing bank.
 16. Thecomputer-readable medium of claim 15, wherein the code for selecting theMID comprises code for determining a ratio of transactions in the firststratum and transactions in the second stratum to assign to the firstMID to achieve the target chargeback rate.
 17. The computer-readablemedium of claim 16, wherein the ratio of transactions is based on apercentage of transactions in the first stratum, a chargeback rate ofthe first stratum, a percentage of transactions in the second stratum,and a chargeback rate of the second stratum.
 18. The computer-readablemedium of claim 16, wherein the code for selecting the MID comprisescode for probabilistically assigning the MID to the transaction based onthe ratio of transactions corresponding to the assigned stratum.
 19. Thecomputer-readable medium of claim 15, further comprising code forreceiving an indication of a decision to approve or decline thetransaction.
 20. The computer-readable medium of claim 19, furthercomprising code for adjusting the target chargeback rate based on theindication of the decision.